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structure physics of exotic nuclei Expertise in scientific computing, including proficiency in C++ or Python programming Fluent oral and written communication skills in English Desired qualifications: It will
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techniques for screening plasmid libraries, such as phage display and yeast display is an advantage. Coding abilities in R/Python are an advantage. The candidate will work in a very ambitious interdisciplinary
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knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant computing language. Experience with machine learning methods is a
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System (ROMS) experience with modelling of marine sediments programming skills in FORTRAN, MATLAB and/or Python experience with super-computers and with the Linux environment an established publication
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is also expected to re-implement historical narrative systems in Python and help determine the course of a larger collaborative CNS project dealing with such systems. About the LEAD AI fellowship
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. Experience in scientific programming (e.g., Matlab and Python) is a requirement. Experience with analysis of climate data sets is an advantage. Applicants must be able to work independently and in a structured
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background in at least two of the following fields: computational physics, porous media physics, statistical physics. Experience with programming language (e.g., Matlab, Python) Fluent oral and written
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Language Processing and LLMs; R; Python. Applicants must be fluent in English. Applicants who have completed their education outside of the EU/EEA-area and who do not have English as a native language, must document
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using Python, R, Matlab, Julia or similar is required. Knowledge of energy systems, energy system modelling or the European energy market will be an advantage. Understanding of atmospheric processes
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strong analytical and problem-solving skills, and be proficient in Python and familiar with relevant programming frameworks and tools. Fluent oral and written communication skills in English The evaluation